Method, device, and program product to monitor the social health of a persistent virtual environment

ABSTRACT

Device, method, and computer program product for monitoring the social health of a persistent virtual environment. The disclosed technology monitors social interactions between subscribers of on-line entities who have interactions related to the persistent virtual environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The following United States patent applications have been filedherewith: U.S. patent application Ser. No. 11/402,399, having the sameinventors, entitled METHOD, DEVICE, AND PROGRAM PRODUCT FOR A SOCIALDASHBOARD ASSOCIATED WITH A PERSISTENT VIRTUAL ENVIRONMENT.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

(Not applicable)

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

(Not applicable)

BACKGROUND

1. Technical Field

The disclosed technology relates to the fields of monitoring persistentvirtual environments and Social Network Analysis of on-line entitieswithin the persistent virtual environment.

2. Background Art

Massively Multiplayer Online Games (MMOGs) are becoming increasinglypopular with subscribers in the millions. The MMOG providers sell aproduct (the software executed on a player's computer) and a service (bymaking a persistent virtual environment available to the account holder)for a periodic subscription fee. Thus, account holder retention isfinancially important to the MMOG provider because the longer the playerassociated with the account holder remains involved with the MMOG, thelonger the MMOG provider receives subscription income. Hence, the MMOGproviders are motivated to create and sustain healthy, long lived,online player communities.

Maintaining and increasing subscriber motivation to continue thesubscription is a major business focus of the game industry. However, nodiagnostic tools are available to timely measure the social aspects ofplayer interactions in the persistent virtual environment or to measureor monitor the health of the online player community in a persistentvirtual environment. While the MMOG provider can gather massive amountsof information about the persistent virtual environment, thisinformation must be analyzed at high cost with, for example, Microsoft®Excel® or SPSS®. Thus, it is difficult, expensive and time consuming forthe MMOG provider to monitor the social health of the persistent virtualenvironment and the analysis results only reflect the state of thepersistent virtual environment at the time the data was collected. Thusthe analysis is not timely, has no capability to forecast problems, andonly operates from single source of information.

Microsoft, Excel, are registered trademarks of Microsoft Corporation ofRedmond Wash. SPSS is a registered trademark of SPSS Inc., of Chicago,Ill.

Marc Smith et al. in Visualization Components for PersistentConversations, Proceedings of CHI'2001 (Seattle Wash., April 1998), ACMPress, pages 136-143, teaches analyzing and presenting a snapshotvisualization of interpersonal connections extracted from newsgroupcommunications.

Warren Sack, in Conversation Map: An Interface for Very-Large-ScaleConversations, Journal of Management Information Systems, Winter 2000,Vol. 17, No. 3, pages 73-92, taught a series of visualization panels toanalyze social interactions in newsgroup to help readers navigate thenewsgroup's information space.

Smith and Sack only teach analyzing data from a single informationsource (the newsgroup).

Nicolas Ducheneaut, in Socialization in an Open Source softwarecommunity: A socio-technical analysis, Computer Supported CooperativeWork, 14(4), pages 323-368, taught techniques for visualizinginteractions between people and interactions between people and things(for example, software code) over time. Ducheneaut teaches accessing avariety of information sources, but is narrowly focused to Open SourceSoftware development projects.

It would be advantageous to provide a way to timely monitor persistentvirtual environments and to measure, monitor, and treat the health ofonline player communities within persistent virtual environments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a networked computer system in accordance with apreferred embodiment;

FIG. 2 illustrates a persistent virtual environment;

FIG. 3 illustrates architecture for a persistent virtual environmentdashboard;

FIG. 4 illustrates a social interaction information dispatch process;

FIG. 5 illustrates social interaction information parser process;

FIG. 6 illustrates the analysis process of FIG. 3;

FIG. 7 illustrates a visualization background process shown in FIG. 3;

FIG. 8 illustrates an instrument selection process;

FIG. 9 illustrates a static metric analysis process;

FIG. 10 illustrates a predictive metric analysis process;

FIG. 11 illustrates a dashboard display process;

FIG. 12 illustrates an instrument process;

FIG. 13 illustrates a dashboard with instruments that indicate aspectsof the persistent virtual environment server's health;

FIG. 14 illustrates a drill-down information instrument of the playerassociation size instrument in FIG. 13;

FIG. 15 illustrates the drill-down information instrument of FIG. 14 inuse to compare the history of the average player association size withthe history of a specified player association size;

FIG. 16 illustrates an instrument that presents the connectivity of themembers of the player association;

FIG. 17 illustrates the instrument shown in FIG. 16 at a second time andthat indicates that the player association is fragmenting; and

FIG. 18 illustrates the instrument shown in FIG. 17 at the second timeconditioned to show the connectivity of the important members of theplayer association.

DETAILED DESCRIPTION

Aspects of the disclosed technology include methods, devices and programproducts that acquire social interaction information between on-lineentities that participate in a persistent virtual environment. Thesocial interaction information is analyzed to create a metric thatrepresents a social aspect of the persistent virtual environment. Avisualization responsive to the metric is presented.

FIG. 1 illustrates a networked computer system 100 that can incorporateand embodiment. The networked computer system 100 includes a computer101 that incorporates a CPU 103, a memory 105, and a network interface107. The network interface 107 provides the computer 101 with access toa network 109. The computer 101 also includes an I/O interface 111 thatcan be connected to a user interface device(s) 113, a storage system115, and a removable data device 117. The removable data device 117 canread a computer-usable data carrier 119 (such as a ROM within thedevice, within replaceable ROM, in a computer-usable data carrier suchas a memory stick, CD, floppy, DVD or any other tangible media) thattypically contains a program product 121. The user interface device(s)113 can include a display device 125. The storage system 115 (along withthe removable data device 117), the computer-usable data carrier 119,and (in some cases the network 109) comprise a file storage mechanism.The program product 121 on the computer-usable data carrier 119 isgenerally read into the memory 105 as a program 123 which instructs theCPU 103 to perform specified operations. In addition, the programproduct 121 can be provided from devices accessed using the network 109.One skilled in the art will understand that the network transmitsinformation (such as data that defines a computer program). Generally,the information is embodied within a carrier-wave. The term“carrier-wave” includes electromagnetic signals, visible or invisiblelight pulses, signals on a data bus, or signals transmitted over anywire, wireless, or optical fiber technology that allows information tobe transmitted from one point to another. Programs and data are commonlyread from both tangible physical media (such as those listed above) andfrom the network 109. Thus, the network 109, like a tangible physicalmedia, is a computer-usable data carrier. One skilled in the art willunderstand that not all of the displayed features of the computer 101need to be present for the all embodiments that implement the techniquesdisclosed herein.

FIG. 2 illustrates a persistent virtual environment 200 that is madeavailable by a virtual world provider 201 that generally programs,maintains, and controls a virtual world server(s) 203 that provides asimulated environment within which players can enter and so becomevirtually present as on-line entities within the persistent virtualenvironment 200. The simulated environment is a persistent virtualenvironment in that the simulated environment is maintained by thevirtual world provider 201 even if no on-line entities are virtuallypresent within the environment. Some examples of persistent virtualenvironments are, without limitation, a multiplayer online game, avirtual office, a virtual resort, a social online world, a onlinelearning system, a virtual classroom, an online training system, anonline collaborative interaction space, and a virtual representation ofa real world attraction. A social online world is, for example, apersistent virtual environment that does not have a game-like goal forthe player but still allows the player to experience new sights andactivities in the persistent virtual environment as well as to developsocial relationships of other players in the persistent virtualenvironment. A collaborative interaction space is a persistent virtualenvironment that allows multiple players to simultaneously navigate aninformation space. The information space can be as broad, for example,as a three dimensional representation of the Internet, or as focused asa three dimensional representation of an object-oriented debuggingsystem.

One aspect of the persistent virtual environment 200 can be a clientdata harvester 207 and/or a provider data harvester 209. These dataharvesters repeatedly gather social interaction information to determinean on-line entity's social ties in the persistent virtual environment200. Additional information can be harvested from the persistent virtualenvironment 200 such as the on-line entity's possessions, location,profession, skills, experience, player association membership etc. Theprovider data harvester 209 generally can access more private accountholder information than can the client data harvester 207 because thevirtual world provider 201 imposes more access restrictions on clientcomputers such as the client data harvester 207.

The term “social interaction information” includes the time theinformation was acquired as well an any type of on-line entity dataacquired by a data harvester, or that can be associated with on-lineentity data acquired by a data harvester. Examples of social interactioninformation include, without limitation, time information, on-lineentity identification information, communication content information,communication type information, communication source information, andaudience identification information.

The client data harvester 207 interfaces to the virtual world server(s)203 through one or more application programming interfaces (API)generally provided or authorized by the virtual world provider 201 (forexample, as a programming interface to a specific computer programminglanguage, or as “macros” provided to automate player actions). However,the client data harvester 207 will generally be limited in the numberand/or type of interactions it can make to the virtual world server(s)203. This can limit the amount of social interaction information thatcan be captured by the client data harvester 207 and/or can extend thetime required to capture the social interaction information. In additionthe client data harvester 207 generally does not have access to anysocial interaction information other than that which would be availableto a player through his/her on-line entity (for example, the client dataharvester 207 generally does not have access to another account holder'saccount information). The provider data harvester 209 however does nothave the performance restrictions of the client data harvester 207 norneed it be restricted from access to account information.

A player in a persistent virtual environment may have multiple personasin that environment. Each persona is a separate on-line entity withinthe persistent virtual environment. Unless the player disclosesplayer-specific information to other players or the MMOG providerprovides facilities to do so, the actions, communications, andcharacteristics of the player's on-line entity is the only source ofinformation available to other on-line entities. Thus, a player refersto an actual person or computerized robot that controls the on-lineentity (such as an avatar) in the persistent virtual environment. Theaccount holder is the real-world entity who has subscribed to thepersistent virtual environment (who may also be a player), the on-lineentity is the avatar in the persistent virtual environment that iscontrolled by the player. On-line entities can establish playerassociations such as guilds, cities, clans, etc. The industry term“player association” is generally an association of on-line entities andnot generally of “players” as these terms are used herein. Playerassociation, as used herein, includes a goal directed temporaryassociation (a “party”—such as a “hunting party”) created by associatingmultiple players who agree to cooperate for a period of time or toachieve a goal.

The player can become virtually present within the persistent virtualenvironment 200 by using a virtual world client 205 to “login” to thepersistent virtual environment 200. In this way, the player controls anon-line entity in the persistent virtual environment 200. The virtualworld client 205 communicates over the network 109 to the virtual worldserver(s) 203 and presents to information about the persistent virtualenvironment 200 to the player (such as by presenting textual, visual,audio, or other information to the player's senses). The player causesthe player's on-line entity to interact with other players' on-lineentities, with non-player entities, or with virtual objects in thepersistent virtual environment 200.

The persistent virtual environment 200 can provide access to newsgroupsand other communication forums (not shown) that are commonly accessiblethrough the network 109. Generally, each of the virtual world server(s)203 has its own separate persistent virtual environment. Some MMOGproviders may provide persistent virtual environments that integrateacross the servers.

FIG. 3 illustrates an architecture for a persistent virtual environmentdashboard 300 that can be used to timely monitor persistent virtualenvironments, and to measure or monitor the health of the online playercommunities within persistent virtual environments. The architecture fora persistent virtual environment dashboard 300 includes a ‘dataacquisition’ process 301, an ‘analysis’ process 303, a ‘visualization’process 305, and a data storage 307. In one embodiment, the ‘dataacquisition’ process 301, the ‘analysis’ process 303 and the‘visualization’ process 305 are individual threads or tasks thatcontinually or periodically execute to store data to, or access datafrom, the data storage 307. The ‘data acquisition’ process 301 canexecute in the client data harvester 207 or in the provider dataharvester 209. The ‘analysis’ process 303 generally executes at aprocessor that has fast access to the data storage 307. The‘visualization’ process 305 can execute at any processor that hasreasonable access to the data storage 307. In addition some of thevisualization processing can be accessed by a remote procedure calls toinvoke remote procedures located at processors that have fast access tothe data storage 307.

The ‘data acquisition’ process 301 monitors one of the virtual worldserver(s) 203 to acquire social interaction information fromcommunications and acts between players of on-line entities in thepersistent virtual environment. The social interaction informationincludes communications made by the players across channels (forexample, those communications made via in-game group chat, privatemessaging, public talk, persistent virtual environment mail messages,and economic transactions). Additional communications can also beobtained from sources outside the persistent virtual environment such asmessage postings on forums related to the persistent virtualenvironment, etc. The ‘data acquisition’ process 301 stores the socialinteraction information, which includes information identifying theon-line entities involved in the communication or act, when the socialinteraction information occurred, and what the social interactioninformation was related to.

For example, the social interaction information can be represented byinteraction data such as:

-   -   “via whisper: on Mar. 15, 2005 Aliceavt sent 3 private messages        to Billavt at 3:13 pm, 3:15 pm, 3:20 pm. The message strings        where ‘foo’, ‘bar’, ‘gizmo’”        or    -   “via action: on Mar. 15, 2005 Aliceavt and Billavt entered        combat”

The ‘data acquisition’ process 301 can be performed by the client dataharvester 207 and/or the provider data harvester 209 and serves toacquire social interaction information from the players throughconversations and acts of their on-line entities as well as from theplayers through off-line communications. The social interactioninformation can be stored in memory or in other accessible storage (suchas the data storage 307).

The ‘analysis’ process 303 can use the stored social interactioninformation as input to various analysis procedures. Each analysisprocedure can generate a metric that represents a social aspect of thepersistent virtual environment. Some embodiments provide a number ofdifferent metrics that represent different social aspects of thepersistent virtual environment. These metrics include those that havebeen identified as representing the “health” of the persistent virtualenvironment.

The ‘visualization’ process 305 presents a visualization responsive tothe metric to provide information about the persistent virtualenvironment. In one embodiment the presented visualization isgraphically represented by one or more visualization panels (forexample, a panel to display prestigious and central on-line entities; apanel to display “hot topics” that identifies most active topics ofconversation between the players; etc. Examples of visualization panelsare provided by FIG. 13 through FIG. 18 with their accompanying text.

In some embodiments, the ‘data acquisition’ process 301 and/or the‘analysis’ process 303 are ongoing processes (for example threads,tasks, etc.) that continually or periodically execute. Otherconceived-of embodiments use event driven invocation of the processes.

In some embodiments, the social interaction information can include apresence state value (such as whether the on-line entity is virtuallypresent when the presence history is gathered). In the case of playerassociations for example, the presence state value can include aparticipation level such as a number or percentage of the playerassociation that are virtually present at the time of acquisition.Furthermore, the presence state value can have a positive or negativevalue with respect to a threshold.

FIG. 4 illustrates a ‘social interaction information dispatch’ process400 that dispatches, for subsequent processing, the social interactioninformation related to on-line entities. Once invoked, the ‘socialinteraction information dispatch’ process 400 initiates at a startterminal 401 and continues to an ‘initialization’ procedure 403 toinitialize variables and/or perform any needed initialization such asallocating resources, establishing network connections, logging in tothe virtual world server(s) 203, etc. Then the ‘social interactioninformation dispatch’ process 400 continues to a ‘receive socialinteraction information’ procedure 405 that can invoke an API or otherinterface technology to access the virtual world server(s) 203 and waitsto receive the social interaction information between an on-line entityand an audience of one or more on-line entities). Once the socialinteraction information is received, a ‘dispatch social interactioninformation to parser thread’ procedure 407 dispatches the socialinteraction information to be processed by a parser (such as issubsequently described with respect to FIG. 5). After dispatching thesocial interaction information, the ‘social interaction informationdispatch’ process 400 returns to the ‘receive social interactioninformation’ procedure 405 to receive the next the social interactioninformation from the virtual world server(s) 203.

The ‘social interaction information dispatch’ process 400 can be invokedas a result of a boot of the client data harvester 207 or the providerdata harvester 209, by an operator or batch command, or in any other wayknown to one skilled in the art. One skilled in the art will understandthat the functionality of the ‘social interaction information dispatch’process 400 can be tightly incorporated into the virtual world server(s)203 itself and need not be performed on a separate processor or as aseparate process.

One embodiment provides a pool of identical threads each of which canparse the social interaction information. In this embodiment, eachthread determines the type of the social interaction information when itis received and parses the social interaction information accordingly.Other embodiments maintain a pools of threads where the threads in eachpool are used to parse a particular type of the social interactioninformation. In these embodiments, the ‘dispatch social interactioninformation to parser thread’ procedure 407 determines the socialinteraction information type and dispatches the social interactioninformation for parsing.

A separate process (not shown) can be used to gather social interactioninformation from sources that are not directly accessible from thepersistent virtual environment (such as a newsgroup or forum related tothe persistent virtual environment). One skilled in the art, afterreading the teachings within, will understand how to access and analyzeinformation from such a source.

FIG. 5 illustrates a ‘social interaction information parser’ process 500that can be invoked by the ‘initialization’ procedure 403 wheninitializing one or more pools of parser threads for use by the ‘socialinteraction information dispatch’ process 400. Once invoked, the ‘socialinteraction information parser’ process 500 initiates at a startterminal 501 and initializes at an ‘initialization’ procedure 503. Onceinitialized, the ‘social interaction information parser’ process 500continues to a ‘wait for dispatch of social interaction information’procedure 505 that pauses the thread until social interactioninformation is dispatched to the thread by the ‘dispatch socialinteraction information to parser thread’ procedure 407. Once the ‘waitfor dispatch of social interaction information’ procedure 505 receivesthe social interaction information, the ‘social interaction informationparser’ process 500 continues to a ‘determine communication source’procedure 507 that determines a communication source for the socialinteraction information; a ‘determine audience’ procedure 509 thatdetermines which on-line entities received the social interactioninformation; and a ‘determine interaction data’ procedure 511 thatextracts interaction data from the relevant the social interactioninformation. Once the ‘determine communication source’ procedure 507,the ‘determine audience’ procedure 509 and the ‘determine interactiondata’ procedure 511 complete, the ‘social interaction informationparser’ process 500 continues to a ‘save interaction data’ procedure 513that saves the parsed social interaction information as the interactiondata in, for example, the storage system 115. Then the ‘socialinteraction information parser’ process 500 continues to the ‘wait fordispatch of social interaction information’ procedure 505 to wait forthe next social interaction information.

The social interaction information is generally text (but can, forexample, be text generated from a speech recognition component) betweenan on-line entity and an audience, as well as descriptive text resultingfrom an emote and/or action command. This synchronous information caninclude, for example but without limitation, identification of theinvolved on-line entities, when the communication was passed, how longthe communication lasted, the communication mode (such as instantmessage, gesture, emote, communication channel (such as a guildcommunication channel)). The social interaction information can also bean asynchronous communication between players either within or externalto the persistent virtual environment such as e-mail, newsgroup or forumpostings as well as user ratings, notes, reviews, group affiliations,rankings and the like. In addition, the social interaction informationcan include demographic information related to account holders. Thesocial interaction information can also include features extracted froman audio or video stream, such as duration, number of phrases, intensityof emotion, etc.

Social interaction information can also be inferred from otherindicators that do not require direct communication between the parties.For example, but without limitation, behavioral information (such asthat information relating to the player participating in jointactivities) can be inferred from a “grouped” marker indicating that twoor more players have formed a party; and from co-location in the gameworld (such as if two players tend to visit the same location in thepersistent virtual environment—such as the same “tavern” in a city—andoptionally overlapping in time). Social interaction information can alsobe inferred from a player's “friend list” (a list of past in-gamecontacts). Social interaction information can also be inferred fromindirect interactions such as when players exchange goods through themediation of an “auction house” or any other in-game marketplace.

The ‘determine communication source’ procedure 507 determines thecommunication source, for example, by determining where the informationwas acquired (such as from a chat channel within the persistent virtualenvironment, or from an external forum or newsgroup). Often thisinformation will be provided within the provided social interactioninformation. Sometimes this information will be deduced by identifyingthe source of the information.

Some embodiments include the capability (for example, by using naturallanguage processing) to determine whether the communication is betweenthe players themselves or between the on-line entities controlled by theplayers (non-role playing communication vs. role playing communication).

The ‘determine audience’ procedure 509 determines the audience, forexample, by tracking which on-line entities are in sufficiently closeproximity to a public communication initiated by an on-line entity usinga particular command mode (for example via a whisper command, a yellcommand, or a default communication command).

The interaction data can also be stored, for example, as a series ofaffiliation matrices where the value at each coordinate (x, y) indicatesthe amount of a given interaction over a given period of time betweenplayers X and Y. This value can be linked to the social interactioninformation used to produce the affiliation matrix for subsequentin-depth analysis. One skilled in the art will understand thatinformation can also be stored using database techniques.

The ‘analysis’ process 303 computes metrics that represent the health ofthe persistent virtual environment. While the following discussion isdirected to examples where the persistent virtual environment is a MMOG,one skilled in the art will understand that the analysis techniquesdisclosed herein apply to any persistent virtual environment thatenables social interaction between the players participating in thepersistent virtual environment as on-line entities.

A metric can be classified as a status metric or a predictive metric. Astatus metric characterizes behavioral information of a set of on-lineentities where the behavioral information is related to at least oneactivity of the on-line entities within the persistent virtualenvironment. Example status metrics include, without limitation, aprominence metric, a centrality degree metric, a cohesive subgroupmetric, a group level equivalence metric, a leadership metric, astrength of ties metric, an interactivity metric, a topic of interactionmetric, a role metric, a social accounting metric, a game-play metric, amodeling metric, and an interaction topic metric as well as othermetrics. These metrics are subsequently briefly described.

The predictive metric can, for example, evaluate the change in a statusmetric over time. Example predictive metrics include, withoutlimitation: a churn metric, a compatibility metric, a scheduling metric,a content consumption metric, an environmental balance metric, asurvival metric, and an economic metric. “Churn” metrics predict theprobability of a player leaving the persistent virtual environment,based on the player's past interaction patterns. Compatibility metricsindicate whether or not a player's playstyle will match a proposedplayer association or whether the player's playstyle matches that of theplayer association to which he/she is a member. Scheduling metricspredict the probability of a player being available for a future jointactivity, so that such activities can be planned in advance. Contentconsumption metrics predict when players will have exhausted thepersistent virtual environment's resources, in order to plan timelyexpansions to the persistent virtual environment. Environmental balancemetrics predict the effects of a design change on the persistent virtualenvironment, such as removing a given “profession” or certain items fromthe persistent virtual environment. Economic metrics predict inflation,trade volume, and other variables in order to maintain the balance oftrade in the persistent virtual environment. The survival metricindicates the health of a player association.

The predictive metrics can include those that develop a time-series ofsingle or combinations of status metrics (weighted or un-weighted) thatrepresent trends; metrics that represent heuristically determinedconditions relevant to the predicted health of the persistent virtualenvironment; metrics based on a vector of attributes representingcharacteristics of the on-line entity such that the vectors can be usedto classify on-line entities and to determine the characteristics ofsuccessful on-line entities; and to determine whether on-line entitieshave characteristics that are progressing toward or away from thecharacteristics of successful on-line entities.

The classification of the on-line entities can utilize a Bayesiannetwork, spectral analysis, nearest neighbor techniques or any otherclassification mechanism.

The predictive metrics can timely measure the social aspects of playerinteractions in the persistent virtual environment, and measure and/ormonitor the health of the online player community in a persistentvirtual environment. By using the predictive metrics, the MMOG providercan alter the persistent virtual environment to encourage desired socialinteractions and to discourage less desired social interactions.

One skilled in the art of Social Network Analysis will understand thatthe metrics can be these used in the Social Network Analysis art. Oneexample of what is known in the art of Social Network Analysis isprovided by Social Network Analysis: Methods and Applications, byWasserman, 2., & Frost, K. (1994), Cambridge University Press. Some ofthese metrics are briefly described below. These metrics allow the MMOGproviders to understand the role played by each player in the persistentvirtual environment. Valuable roles (e.g. leaders holding groups ofplayers together, thereby increasing the quality of their socialexperience in the persistent virtual environment) can be encouraged withincentives; and problematic roles (for example, players disrupting agroup's cohesion) can be eliminated. The following metrics can allassist in detecting these roles.

A “prominence” metric represents the ties to the on-line entity thatmake the on-line entity particularly visible to other on-line entitiesin the persistent virtual environment. The prominence metric is computedby evaluating direct and adjacent ties, and also indirect pathsinvolving intermediaries.

A “centrality degree” metric indicates the amount the other on-lineentities recognize that a specific on-line entity is well socially wellconnected and serves as a major information channel.

A “cohesive subgroup” metric indicates subsets of on-line entities amongwhom there are relatively strong, direct, intense, frequent or positiveties (for example, mutuality of ties, frequency of ties, frequency ofties among on-line entities within the subgroup as compared to on-lineentities who are not within the subgroup). In this context, a “tie” is away of representing a social relationship between two entities, wherethe relationship is based on some type of information transfer(symmetric or asymmetric) between the entities.

A “leadership” metric can be computed from standard social networkmetrics such as prestige and centrality.

A “strength of ties” metric represents the density of connections withinclusters of on-line entities, the size of these clusters, and theoverall social network structure.

An “interactivity” metric represents the average number of utterances,average size of utterance, average length of interactions, number ofunique versus repeated utterances and interactions, etc. This metric canbe determined relevant to a specific one of, or subset of, on-lineentities. It can also be determined relevant to a specific location inthe persistent virtual environment.

A “social accounting” metric indicates an on-line entity's socialimportance (as determined, for example, from network centrality, rate ofadvancement, items they own or other heuristically determinedcharacteristic).

A “topics of interaction” metric can be obtained from text mining of theon-line entity's utterances. their bios, or from the forums, etc. Thetopics can be determined by using natural language processing. Forexample, one can perform topic extraction from the corpus of textmessages typed by the players based on a thesaurus. One can also build asemantic network by extracting words that frequently co-occur, therebyidentifying associations between concepts as expressed by the players(for example, if the words “paladin”—a character class—and “unbalanced”frequently co-occur, this might be an indication of a design flaw in thepersistent virtual environment).

A “role” metric can be developed from combinations of the above metrics.For example, an on-line entity can serve one or more roles. These rolescan include “helper,” “expert,” “philosopher,” “comic,” “griefer,”“facilitator,” “socializer”, etc.

A “gameplay” metric indicates the number of on-line entities within agiven character class, having a given level, within a persistent virtualenvironment geographical zone, those owing particular articles etc.

The metrics allow time series analysis of aspects of the persistentvirtual environment based on where and when on-line entities arevirtually present and so enables the MMOG provider to recommend and/orencourage formation of optimal groupings of on-line entities (forexample by encouraging development of a player association or tooptimize load across servers based on activities of on-line entities).

The metrics also allow the MMOG provider to model the impact ofmodifications to the persistent virtual environment by predictingconsequences of the modification. In addition, the social accountingmetric can be used to 1) prioritize the work of “customer servicerepresentatives” (the people who handle complaints)—important playersget VIP treatment; 2) target key players for retention if they show anysign of leaving persistent virtual environment.

The metrics also allow the MMOG provider to detect and remove griefersor other players who exhibit fraudulent and/or deviant behavior.

FIG. 6 illustrates an analysis process 600 that can be used for the‘analysis’ process 303 of FIG. 3. The analysis process 600 initiates ata start terminal 601. Once initiated, the analysis process 600 continuesto an ‘initialization’ procedure 603 that performs any requiredinitialization. An ‘invoke analysis module threads’ procedure 605 canthen invoke analysis threads such as the thread described with respectto FIG. 9 and/or FIG. 10. A ‘find new interaction data’ procedure 607determines what new interaction data has been gathered since itsprevious execution. Each static metric analysis thread is iterated by an‘iterate over static metric analysis modules’ procedure 609 and a dataupdate event is posted to the iterated thread by the ‘post data updateto static metric analysis thread’ procedure 611.

Once all the data update events have been posted to the static metricanalysis threads, a ‘wait for static metric analysis threads’ procedure613 waits for the static metric analysis threads to completeincorporating the new social interaction information into the metricscomputed by the static metric analysis modules implemented by the staticmetric analysis threads. Once all the static metric analysis threadscomplete processing, the analysis process 600 continues to an ‘iterateover predictive metric analysis modules’ procedure 615. The ‘iterateover predictive metric analysis modules’ procedure 615 iterates eachpredictive metric analysis thread. A ‘post data update to predictivemetric analysis thread’ procedure 617 posts a data update event to theiterated thread so that the corresponding predictive metric analysismodule can process the new social interaction information in light ofchanges in the static metric analysis metrics (and/or in light of thenew interaction data). The analysis process 600 then continues to a‘wait for predictive metric analysis threads’ procedure 619 to wait forthe predictive metric analysis threads to complete. Once they complete,the analysis process 600 loops back to the ‘find new interaction data’procedure 607.

The ‘find new interaction data’ procedure 607 can include a delay tolimit the computational burden of the analysis process 600.

FIG. 7 illustrates a visualization background process 700 that can beused as the ‘visualization’ process 305 shown in FIG. 3. Thevisualization background process 700 can be invoked when the systemstarts, initiates at a start terminal 701, and is initialized by an‘initialization’ procedure 703. An ‘invoke dashboard’ procedure 705invokes the dashboard display thread that is subsequently described withrespect to FIG. 11. An ‘iterate selected instruments’ procedure 707iterates each instrument that has been selected for display. For eachiterated instrument, an ‘invoke instrument’ procedure 709 starts athread associated with that instrument as is subsequently described, forexample, with respect to FIG. 12. The visualization background process700 completes through an end terminal 711.

The instrument selection presented within the dashboard can be specifiedby a preference, user command or other techniques known in the art. Forexample, FIG. 8 illustrates an instrument selection process 800 that canbe used to change the presented instruments. The instrument selectionprocess 800 can be invoked by a user prior to, or subsequent to invokingthe visualization background process 700. The instrument selectionprocess 800 initiates at a start terminal 801, receives the user'sinstrument selection at a ‘receive user instrument selection’ procedure803 and modifies the instrument preferences specification if needed by a‘modify instrument selection’ procedure 805. If the dashboard has beeninvoked by the ‘invoke dashboard’ procedure 705, an ‘affect instrumentchange’ procedure 807 posts the required events to the dashboard toeffect the user's change of the instrument. The instrument selectionprocess 800 completes through an end terminal 811.

FIG. 9 illustrates a static metric analysis process 900 that can be usedto compute one or more static metric values. The static metric analysisprocess 900 can be invoked by the ‘invoke analysis module threads’procedure 605, initiates at a start terminal 901, initializes at an‘initialization’ procedure 903 and waits for a data update event fromthe ‘post data update to static metric analysis thread’ procedure 611 ofFIG. 6 at a ‘wait for data update event’ procedure 905. Once the dataupdate event is caught, the ‘wait for data update event’ procedure 905drains any other pending data update events and continues to an‘incorporate data’ procedure 907 that accesses the new interaction datastored by the ‘save interaction data’ procedure 513 between the previousand current execution of the static metric analysis process 900. The‘incorporate data’ procedure 907 processes the new interaction data and,optionally, any other data required to compute the static metric. A‘generate metric’ procedure 909 generates the static metric from theinformation provided by the ‘incorporate data’ procedure 907. The staticmetric is time-stamped and stored by a ‘save time stamped metric’procedure 911, which makes the static metric available to other threads.A ‘post instrument update event’ procedure 913 then posts an instrumentupdate event to cause any instrument using the static metric to updateits presentation. The static metric analysis process 900 returns to the‘wait for data update event’ procedure 905 for further processing.

Storing a time-stamped static metric as a snapshot enables subsequentanalysis on the metric at a particular time, by aggregating severalsnapshots and performing statistical analysis of the metric, or bydetermining a predictive metric based on how a static metric changesover time.

FIG. 10 illustrates a predictive metric analysis process 1000 that canbe used to compute one or more predictive metric values. The predictivemetric analysis process 1000 can be invoked by the ‘invoke analysismodule threads’ procedure 605, initiates at a start terminal 1001,initializes at an ‘initialization’ procedure 1003 and waits for a dataupdate event from the ‘post data update to predictive metric analysisthread’ procedure 617 of FIG. 6 at a ‘wait for data update event’procedure 1005. Once the data update event is caught, the ‘wait for dataupdate event’ procedure 1005 drains any other pending data update eventsand continues to an ‘update time-series with prior analysis’ procedure1007 that can access the new social interaction information stored bythe ‘save interaction data’ procedure 513 between any prior and currentexecution of the predictive metric analysis process 1000. The ‘updatetime-series with prior analysis’ procedure 1007 processes the new socialinteraction information and, optionally, any other data required togenerate the predictive metric (for example, but without limitation, oneor more of the static metrics). A ‘generate predictive metric’ procedure1009 generates the predictive metric from information provided by the‘update time-series with prior analysis’ procedure 1007. The predictivemetric is time-stamped and stored by a ‘save time stamped predictivemetric’ procedure 1011, which makes the predictive metric available toother threads. A ‘post instrument update event’ procedure 1013 thenposts an instrument update event to cause any instrument using thepredictive metric to update its presentation. The predictive metricanalysis process 1000 returns to the ‘wait for data update event’procedure 1005 for further processing.

One skilled in the art will understand that the ‘wait for data updateevent’ procedure 905 and the ‘wait for data update event’ procedure 1005may include dependency on other events, clocks, or intervals to limitthe computational burden used to generate the static metric orpredictive metric.

One skilled in the art will understand that the ‘post instrument updateevent’ procedure 913 and the ‘post instrument update event’ procedure1013 can also provide information used by the ‘wait for static metricanalysis threads’ procedure 613 and the ‘wait for predictive metricanalysis threads’ procedure 619 respectively to indicate completion ofthe thread to the analysis process 600.

The ‘update time-series with prior analysis’ procedure 1007 and the‘update time-series with prior analysis’ procedure 1007 can, for manystatic metrics and predictive metrics can incrementally update themetrics with the new interaction data without needing to reprocess alarge portion of the of the historical interaction data. However, forsome of the metrics, the historical interaction data and the newinteraction data are concatenated and the corresponding metrics computedfrom the complete or some portion of the interaction data.

The stored time-stamped metrics provide snapshots of the metric at aparticular time and snapshots taken at different times can be compared.These snapshots also can be correlated to a time series to monitor howthe metrics change over time, and/or to provide predictions based ontheir trends. Furthermore, the metrics can be aggregated to providestatistically significant data that indicates key relationships betweenthe metrics (a compound metric). An example of a compound metric is a“survival” metric that can be computed by using regression techniques onweighted factors that contribute to a player association's survival. Thesurvival metric can be determined from the player association size,density, and the number of isolated subgraphs in a social network (theisolated subgraphs in the social network represent independent subgroupsin a social unit).

FIG. 11 illustrates a dashboard display process 1100 that can be invokedby the ‘invoke dashboard’ procedure 705 of FIG. 7, initiates at a startterminal 1101, and is initialized by an ‘initialization’ procedure 1103.Once initialized, a ‘present dashboard window’ procedure 1105 presentsthe background dashboard image and an ‘allocate instrument panes’procedure 1107 allocates default panes for presentation of instrumentreadings. Next, the dashboard display process 1100 waits for atermination event at a ‘wait for termination event’ procedure 1109 andwhen a termination event is caught the ‘wait for termination event’procedure 1109 continues to the ‘terminate instrument’ procedure 1111that posts termination events to each of the instruments registered withthe dashboard and, after the instruments terminate, the dashboarddisplay process 1100 completes through an end terminal 1113.

The default panes can be deleted, reconfigured, repositioned, resizedetc. as needed to accommodate the selected instruments.

FIG. 12 illustrates an instrument process 1200 that can be invoked bythe ‘invoke instrument’ procedure 709, that initiates at a startterminal 1201 and initializes at an ‘initialization’ procedure 1203.Once initialized, the instrument process 1200 continues to a ‘registerwith dashboard and obtain pane’ procedure 1205 that registers theinstrument process 1200 with the dashboard display process 1100 andobtains a pane from the dashboard within which the instrument process1200 can present a visual indicator. Once the pane is obtained, a‘present instrument background’ procedure 1207 presents the instrumentbackground in the pane. The instrument process 1200 continues to a ‘waitfor instrument update event’ procedure 1209 that waits for an instrumentupdate event such as provided by the ‘post instrument update event’procedure 1013 that indicates the presented metric has changed. Once theinstrument update event is caught, an ‘access metric data’ procedure1211 accesses the metric data for the instrument and a ‘presentinstrument reading’ procedure 1213 computes the value to be displayed asa reading from one or more of the metrics and presents the instrumentreading over the background provided by the ‘present instrumentbackground’ procedure 1207. After presenting the instrument reading, theinstrument process 1200 continues to the ‘wait for instrument updateevent’ procedure 1209 to await the next instrument update event.

One skilled in the art will understand that the instrument process 1200can be terminated responsive to a termination event. Such a one willalso understand that there are many ways to present the dashboard thatinclude, for example, displaying the dashboard and instruments on amonitor, on a tangible media such as paper, as a video to a tangiblestorage media etc. In addition, the instrument process 1200 can alsopost an alert indication such as by posting an event to an appropriateprocess, by altering the visual indicator to show the alert indication,by invoking an audible alert indication, presenting reports, displays,etc.

One aspect of the technology provides diagnostic tools to the MMOGprovider that diagnose and alter the persistent virtual environment tomaintain and/or increase the subscriber motivation. By recognizinginfluential players or other on-line entities, and by being able topredict problems in the persistent virtual environment, the MMOGprovider can take proactive actions to improve the health of thepersistent virtual environment. Some of the diagnostic tools presentinstrument readings based on one or more of the previously describedmetrics.

These diagnostic tools allow the MMOG provider to explore therelationships between on-line entities (for example using network graphssuch as shown in FIG. 15 through FIG. 18). The MMOG provider can alsochoose which interactions to observe at some level of granularity andfor what period of time. The diagnostic tools provide the capability ofidentifying socially influential players and of recommending how theMMOG provider can encourage the identified players to actions that willimprove the health of the persistent virtual environment. The toolsallow the MMOG provider to investigate the rational and data thatsupports the tool's recommendation. Other diagnostic tools predict thehealth of the persistent virtual environment if the recommendations is,or is not, followed.

Some of the diagnostic tools are instruments that provide a high-levelreading of the health of the persistent virtual environment or of theplayer associations within the persistent virtual environment. Anexample of such an instrument is one that displays the overallinteractivity level of the on-line entities within the persistentvirtual environment. Another example presents an overview of the moreimportant metrics for the persistent virtual environment. One suchmetric could be those that are relevant to player associations (forpersistent virtual environments where the aggregate health of the playerassociations correlate with subscriber retention).

The diagnostic tools can also provide warnings and alerts if there aresignificant changes in the social environment in the persistent virtualenvironment (for example, if the number of on-line entities classifiedas “leaders” suddenly drops).

Other diagnostic tools can detect problematic trends. One example wouldbe that of detecting the deteriorating health of a player association,or of providing a list of player associations that are in jeopardy ofdisappearing. Other diagnostic tools can be used to determine the causeof a player association being in jeopardy and can recommend remedialaction for the MMOG provider to improve the situation. Yet otherdiagnostic tools can monitor and predict subscriber churn and retentionrates. Other tools allow MMOG provider to detect and police undesirablesocial behavior of a player in the persistent Virtual environment.

FIG. 13 illustrates an instrumented dashboard 1300. This particularrepresentation includes a guild size instrument 1301, a subgroupinstrument 1303, a guild density instrument 1305, a legend 1307 and apersistent virtual environment identification 1309. The persistentvirtual environment identification 1309 identifies the persistentvirtual environment that is being measured by the presented instruments.The legend 1307 identifies the Critical, Danger, and Normal zones of theinstrument readings of the metric. In most embodiments, these zones arecolor coded. In FIG. 13 the different colors are indicated by differenttextures. The guild size instrument 1301 represents the metric thatindicates the number of on-line entities who have joined a playerassociation. The subgroup instrument 1303 represents the metric thatindicates the fragmentation of the player associations. The guilddensity instrument 1305 represents the metric that indicates theconnections between the on-line entities within the player associations.

The instrumented dashboard 1300 also includes a command bar 1311 thatcontains one or more controls such as a user activated control 1313 thatcan be activated by a user to send a selection command to presentdrill-down information about an aspect of one if the presentedinstruments. One skilled in the art will understand that there are manyways to select which instrument to provide drill-down information about.These include selecting the instrument and invoking the selectioncommand on the selected instrument, as well as activating a control thatinvokes the selection command specific to an instrument (such as theuser activated control 1313 that specifically drills-down into the guildsize instrument 1301). The instrument image itself can also serve as theuser activated control 1313.

The Critical, Danger, and Normal zones on instruments can representcomputed thresholds. We perform a regression of several factors over atarget-variable (one embodiment, performs a regression of the playerassociation size metric, the player association density metric, and thenumber of isolated subgraphs in the player association, over asurvival-variable to measure the influence of each listed metric overthe survival of a player association). Using the equation resulting fromthe regression, the value of each factor at which the target variablechanges is computed. These values are used as thresholds for theinstrument (for example in on persistent virtual environment, when theplayer association size metric is more than 10, all other metric beingequal, player associations will survive; when the player associationsize metric less than 5, player associations will die). These values andmetric can be used to prioritize and route customer support requests tothe appropriate entity at the MMOG provider who are appropriatelyempowered to handle the request. The support entities can also usetechnology described herein to understand the importance to thepersistent virtual environment of the player who has made the supportrequest and thus, to respond accordingly. Furthermore, leadingindicators responsive to enhancements or changes are made to thepersistent virtual environment can be monitored to allow timely removeor fix to the enhancement if there are unexpected results.

FIG. 14 illustrates a drill-down information instrument 1400 related tothe guild size instrument 1301 of FIG. 13 and shows a metric history1401, an attention needed area 1403, a player association selectioncontrol 1405, and a threshold indicator 1407. The drill-down informationinstrument 1400 can be invoked from the user activated control 1313. Themetric history 1401 presents the value of the metric over time. Theattention needed area 1403 lists player associations that are in dangerof disbanding as well as those that are critically close to disbanding.In one embodiment, the user can select any player association listed inthe attention needed area 1403 to drill-down into the selected playerassociation that needs attention. The player association selectioncontrol 1405 allows a user to display the historical metric for anyspecified player association(s). The threshold indicator 1407 in themetric history 1401 indicates values of the metric that are significant(for example, see the discussion related to FIG. 13. A playerassociation size history curve 1409 plots the daily size of the playerassociations in the persistent virtual environment and, optionally, asmoothed mean value. The attention needed area 1403 being emptyindicates that no player association are in danger of disbanding.

FIG. 15 illustrates a drill-down information instrument 1500 as in FIG.14 at a different time after a user has selected a user specified playerassociation 1501 (named “Amor E Morte”) from the player associationselection control 1405 or from the “In Danger” pane of the attentionneeded area 1403. In one embodiment, the player association size historycurve 1409 can be compared to a specified player association sizehistory curve 1509 that plots the daily size of the specified playerassociation and, optionally, a mobile mean value. Player associationscan automatically be entered into the attention needed area 1403 bydetermining the trend of the predictive metric associated with thedrill-down. In this figure, a number of player associations are indanger of disbanding and others are critically close to disbanding (asdetermined by the trend of the predictive metric associated with thesize of the player association).

Once a player association is identified as needing attention, thepersistent virtual environment provider can examine the history of theassociated predictive metrics and static metrics to determine how bestto correct the problem. For example, when the problem is with a playerassociation, the persistent virtual environment provider can investigatethe connectivity metrics associated with the player association.

FIG. 16 illustrates a member connectivity instrument 1600 that presentsdrill-down information about a selected player association (in thisfigure, the name of the player association is “An Immense Waste ofTime”—the player association name is agreed upon by the players who formthe player association and this name could be any other string). Thedrill-down information is provided (by this instrument) as aconnectivity network 1601 that represents the ties (connections) thateach member in the player association has with the other members. Forexample, a less connected member 1603 has few connections to othermembers while a more connected member 1605 has many connections to othermembers (and has high scores on other important metrics which isrepresented by the placement of this member in the center of theconnectivity network 1601). The member connectivity instrument 1600 canalso include a playback control bar 1607 and a playback time indicator1609 to allow a user to observe how the connectivity network 1601changes over time. The member connectivity instrument 1600 can alsoinclude a member selection control 1611 that allows identification of,and drill-down capability to individual members of the playerassociation; and a filter selection control 1613 that allows a user toselect the amount of, and what type of information is presented in themember connectivity instrument 1600.

FIG. 17 illustrates the member connectivity instrument 1600 whenpresenting the connectivity network 1601 at a second time as indicatedby the slider on the playback time indicator 1609. At this time, it isclear that the more connected member 1605 of the player association isthe only tie between a first player association fragment 1703 and asecond player association fragment 1705. The smaller the fragment, themore likely the members of the fragment will leave the playerassociation and possibly the persistent virtual environment. The playerassociation is likely to disband if the more connected member 1605 wereto leave the player association. In addition, the more connected member1605 is no longer in the center of either fragment (as was the case inFIG. 16) and thus, appears to have lost his leadership role in theplayer association.

Comparing the connectivity network 1601 of FIG. 16 with that of FIG. 17the virtual world provider 201 can determine that a once healthy playerassociation is starting to fragment. Thus, the virtual world provider201 has now identified the pivotal player (or players) that can beinfluenced to help the player association recover. This identificationcan be through the use of the member selection control 1611. Onselection of the more connected member 1605 in the member selectioncontrol 1611 the system will provide a recommendation to the virtualworld provider 201 on how to influence or pressure the more connectedmember 1605 to continue in the player association.

The marketing department of a MMOG provider initially providesrecommended actions Possible recommendation actions would initially beprovided using heuristics. For example the initial recommendationactions for increasing player retention could include offering thesubscriber: free subscription periods, free items (both in thepersistent virtual environment and/or in the real world), an invitationto a special event, or any other retention incentive). The persistentvirtual environment then tracks the trajectory of each player afterreceipt of the retention incentive and measures the effectiveness ofeach retention incentive and adjusts its recommendations accordingly(individually to the tracked player and/or to classes of players).

FIG. 18 illustrates the member connectivity instrument 1600 whenpresenting the connectivity network 1601 at a second time but with afilter selected by the filter selection control 1613 to present asimplified representation of the connectivity network 1601 shown in FIG.17. In this instance, the simplified representation only includesmembers who have more than a specific number of connections. Thisrepresentation allows the virtual world provider 201 to identify themost connected members of the player association and to take pro-activemeasures to engage them in the persistent virtual environment.

As used herein, a procedure is a self-consistent sequence of steps thatcan be performed by logic implemented by a programmed computer,specialized electronics or other circuitry or a combination thereof thatlead to a desired result. These steps can be defined by one or morecomputer instructions. These steps can be performed by a computerexecuting the instructions that define the steps. Further, these stepscan be performed by circuitry designed to perform the steps. Thus, theterm “procedure” can refer (for example, but without limitation) to asequence of instructions, a sequence of instructions organized within aprogrammed-procedure or programmed-function, a sequence of instructionsorganized within programmed-processes executing in one or morecomputers, or a sequence of steps performed by electronic or othercircuitry, or any logic. Thus, for example, a data acquisition logic caninclude custom electronics, a processor unit executing storedinstructions, or any combination thereof.

One skilled in the art will understand that the network transmitsinformation (such as informational data as well as data that defines acomputer program). The information can also be embodied within acarrier-wave. The term “carrier-wave” includes electromagnetic signals,visible or invisible light pulses, signals on a data bus, or signalstransmitted over any wire, wireless, or optical fiber technology thatallows information to be transmitted over a network. Programs and dataare commonly read from both tangible physical media (such as a compact,floppy, or magnetic disk) and from a network. Thus, the network, like atangible physical media or other article of manufacture, can beconsidered a computer-usable data carrier.

One skilled in the art will understand that the technology enables aMMOG provider to timely monitor persistent virtual environments and tomeasure and/or monitor the health of the online player communitieswithin the persistent virtual environments.

From the foregoing, it will be appreciated that the technology enables aMMOG provider to (without limitation):

-   -   1) collect, track, and monitor social data of on-line entities        and/or players in a persistent virtual environment;    -   2) track the health of social communities within a persistent        virtual environment;    -   3) to model recommendations to improve game design;    -   4) model and predict subscriber churn and retention rates;    -   5) initiate real-time, tactical marketing programs to retain key        social players and reduce subscriber churn;    -   6) police undesirable social behavior;    -   7) prioritize and route customer service requests; and    -   8) provide timely feedback on the impact of persistent virtual        environment enhancements.

The claims, as originally presented and as they may be amended,encompass variations, alternatives, modifications, improvements,equivalents, and substantial equivalents of the embodiments andteachings disclosed herein, including those that are presentlyunforeseen or unappreciated, and that, for example, may arise fromapplicants/patentees and others.

1. A computer controlled method comprising: acquiring social interactioninformation, from a social interaction between two or more on-lineentities that participate in a persistent virtual environment, withoutrequiring an input from a user of the persistent virtual environment;analyzing said social interaction information to determine a metric thatrepresents a social aspect of said persistent virtual environment,wherein the determined metric includes a predictive metric that predictsa future trend on the persistent virtual environment; and presenting avisualization responsive to said metric.
 2. The computer controlledmethod of claim 1, wherein said metric is a status metric thatcharacterizes behavioral information of one or more of said plurality ofon-line entities, said behavioral information related to at least oneactivity of said one or more of said plurality of on-line entitieswithin said persistent virtual environment.
 3. The computer controlledmethod of claim 2, wherein said status metric is selected from one ormore of the group consisting of a prominence metric, a centrality degreemetric, a cohesive subgroup metric, a group level equivalence metric, aleadership metric, a strength of ties metric, an interactivity metric, atopic of interaction metric, a role metric, a social accounting metric,a game-play metric, a modeling metric, and an interaction topic metric.4. The computer controlled method of claim 1, wherein said predictivemetric is selected from one or more of the group consisting of a churnmetric, a compatibility metric, a scheduling metric, a contentconsumption metric, an environmental balance metric, a survival metric,and an economic metric.
 5. The computer controlled method of claim 1,wherein said predictive metric is computed responsive to a time seriesand at least one status metric, and wherein said at least one statusmetric characterizes behavioral information of one or more of saidplurality of on-line entities, said behavioral information related to atleast one activity of said one or more of said plurality of on-lineentities within said persistent virtual environment.
 6. The computercontrolled method of claim 1, further comprising storing a snapshot ofsaid metric.
 7. The computer controlled method of claim 6, whereinpresenting said visualization further comprises aggregating saidsnapshot with a second snapshot.
 8. The computer controlled method ofclaim 6, wherein presenting said visualization further comprisesdetermining a change in said metric from said snapshot to a secondsnapshot.
 9. The computer controlled method of claim 1, wherein saidsocial interaction information comprises one or more of a groupconsisting of time information, on-line entity identificationinformation, communication content information, communication typeinformation, communication source information, and audienceidentification information.
 10. The computer controlled method of claim1, wherein presenting said visualization further comprises presenting analert indication.
 11. The computer controlled method of claim 1, whereinsaid visualization indicates an overall interactivity level of saidplurality of on-line entities within said persistent virtualenvironment.
 12. The computer controlled method of claim 1, wherein saidvisualization represents behavioral information of one or more of saidplurality of on-line entities, said behavioral information related to atleast one activity of said one or more of said plurality of on-lineentities within said persistent virtual environment.
 13. The computercontrolled method of claim 1, wherein said persistent virtualenvironment has one or more characteristics of a group consisting of amultiplayer online game, a virtual office, a virtual resort, a socialonline world, a online learning system, a virtual classroom, an onlinetraining system, an online collaborative interaction space, and avirtual representation of a real world attraction.
 14. An apparatushaving a processing unit and a memory coupled to said processing unitcomprising: a data acquisition logic configured to acquire socialinteraction information, from a social interaction between two or moreon-line entities that participate in a persistent virtual environment,without requiring an input from a user of the persistent virtualenvironment; an analysis logic configured to analyze said socialinteraction information acquired from the data acquisition logic todetermine a metric that represents a social aspect of said persistentvirtual environment, wherein the determined metric includes a predictivemetric that predicts a future trend on the persistent virtualenvironment; and a presentation logic configured to present avisualization responsive to said metric.
 15. The apparatus of claim 14,wherein said metric is a status metric that characterizes behavioralinformation of one or more of said plurality of on-line entities, saidbehavioral information related to at least one activity of said one ormore of said plurality of on-line entities within said persistentvirtual environment.
 16. The apparatus of claim 15, wherein said statusmetric is selected from one or more of the group consisting of aprominence metric, a centrality degree metric, a cohesive subgroupmetric, a group level equivalence metric, a leadership metric, astrength of ties metric, an interactivity metric, a topic of interactionmetric, a role metric, a social accounting metric, a game-play metric, amodeling metric, and an interaction topic metric.
 17. The apparatus ofclaim 14, wherein said predictive metric is selected from one or more ofthe group consisting of a churn metric, a compatibility metric, ascheduling metric, a content consumption metric, an environmentalbalance metric, a survival metric, and an economic metric.
 18. Theapparatus claim 14, wherein said predictive metric is computedresponsive to a time series and at least one status metric, and whereinsaid at least one status metric characterizes behavioral information ofone or more of said plurality of on-line entities, said behavioralinformation related to at least one activity of said one or more of saidplurality of on-line entities within said persistent virtualenvironment.
 19. The apparatus of claim 14, further comprising storagelogic configured to store a snapshot of said metric.
 20. The apparatusof claim 19, wherein the presentation logic further comprises anaggregation logic configured to aggregate said snapshot with a secondsnapshot.
 21. The apparatus of claim 19, wherein the presentation logicfurther comprises a difference logic configured to determine a change insaid metric from said snapshot to a second snapshot.
 22. The apparatusof claim 14, wherein said social interaction information comprises oneor more of a group consisting of time information, on-line entityidentification information, communication content information,communication type information, communication source information, andaudience identification information.
 23. The apparatus of claim 14,wherein the presentation logic further comprises an alert logicconfigured to present an alert indication.
 24. The apparatus of claim14, wherein said visualization indicates an overall interactivity levelof said plurality of on-line entities within said persistent virtualenvironment.
 25. The apparatus of claim 14, wherein said visualizationrepresents behavioral information of one or more of said plurality ofon-line entities, said behavioral information related to at least oneactivity of said one or more of said plurality of on-line entitieswithin said persistent virtual environment.
 26. The apparatus of claim14, wherein said persistent virtual environment has one or morecharacteristics of a group consisting of a multiplayer online game, avirtual office, a virtual resort, a social online world, a onlinelearning system, a virtual classroom, an online training system, anonline collaborative interaction space, and a virtual representation ofa real world attraction.
 27. A computer program product comprising: acomputer-usable data carrier providing instructions that, when executedby a computer, cause said computer to perform a method comprising:acquiring social interaction information, from a social interactionbetween two or more on-line entities that participate in a persistentvirtual environment, without requiring an input from a user of thepersistent virtual environment; analyzing said social interactioninformation to determine a metric that represents a social aspect ofsaid persistent virtual environment, wherein the determined metricincludes a predictive metric that predicts a future trend on thepersistent virtual environment; and presenting a visualizationresponsive to said metric.
 28. The computer program product of claim 27,wherein said metric is a status metric that characterizes behavioralinformation of one or more of said plurality of on-line entities, saidbehavioral information related to at least one activity of said one ormore of said plurality of on-line entities within said persistentvirtual environment.
 29. The computer program product of claim 28,wherein said status metric is selected from one or more of the groupconsisting of a prominence metric, a centrality degree metric, acohesive subgroup metric, a group level equivalence metric, a leadershipmetric, a strength of ties metric, an interactivity metric, a topic ofinteraction metric, a role metric, a social accounting metric, agame-play metric, a modeling metric, and an interaction topic metric.30. The computer program product of claim 27, wherein said predictivemetric is selected from one or more of the group consisting of a churnmetric, a compatibility metric, a scheduling metric, a contentconsumption metric, an environmental balance metric, a survival metric,and an economic metric.
 31. The computer program product of claim 27,wherein said predictive metric is computed responsive to a time seriesand at least one status metric, and wherein said at least one statusmetric characterizes behavioral information of one or more of saidplurality of on-line entities, said behavioral information related to atleast one activity of said one or more of said plurality of on-lineentities within said persistent virtual environment.
 32. The computerprogram product of claim 27, further comprising storing a snapshot ofsaid metric.
 33. The computer program product of claim 32, whereinpresenting said visualization further comprises aggregating saidsnapshot with a second snapshot.
 34. The computer program product ofclaim 32, wherein presenting said visualization further comprisesdetermining a change in said metric from said snapshot to a secondsnapshot.
 35. The computer program product of claim 27, wherein saidsocial interaction information comprises one or more of a groupconsisting of time information, on-line entity identificationinformation, communication content information, communication typeinformation, communication source information, and audienceidentification information.
 36. The computer program product of claim27, wherein presenting said visualization further comprises presentingan alert indication.
 37. The computer program product of claim 27,wherein said visualization indicates an overall interactivity level ofsaid plurality of on-line entities within said persistent virtualenvironment.
 38. The computer program product of claim 27, wherein saidvisualization represents behavioral information of one or more of saidplurality of on-line entities, said behavioral information related to atleast one activity of said one or more of said plurality of on-lineentities within said persistent virtual environment.
 39. The computerprogram product of claim 27, wherein said persistent virtual environmenthas one or more characteristics of a group consisting of a multiplayeronline game, a virtual office, a virtual resort, a social online world,a online learning system, a virtual classroom, an online trainingsystem, an online collaborative interaction space, and a virtualrepresentation of a real world attraction.